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An Effective EM-PND Based Integrated Approach for NRSFM with Small Size Sequences

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Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

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Abstract

The performance of non-rigid structure from motion (NRSFM) generally deteriorates when the image sequence is small. In this paper, an effective approach is proposed to deal with NRSFM with small size sequences based on the Expectation and Maximization-Procrustean Normal Distribution (EM-PND) algorithm. In the proposed method, the sub-sequences are first extracted from the original small size sequence. Further, some weaker estimators are constructed by inputting the sub-sequences to the EM-PND algorithm. Finally, the 3Dstructures of the sequences are estimated by integrating the outputs of these weaker estimators. Experimental results on several widely used sequences demonstrate the effectiveness and feasibility of the proposed algorithm.

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References

  1. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  2. Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. Comput. Vis. 9(2), 137–154 (1992)

    Article  Google Scholar 

  3. Bregler, C., Hertzmann, A., Biermann, H.: Recovering non-rigid 3D shape from image streams. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 690–696 (2000)

    Google Scholar 

  4. Akhter, I., Sheikh, Y., Khan, S.: Computational prediction models for early detection of risk of cardiovascular events using mass spectrometry data. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1534–1541 (2009)

    Google Scholar 

  5. Hamsici, O.C., Gotardo, P.F.U., Martinez, A.M.: Learning spatially-smooth mappings in non-rigid structure from motion. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 260–273. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33765-9_19

    Chapter  Google Scholar 

  6. Torresani, L., Hertzmann, A., Bregler, C.: Nonrigid structure-from-motion: estimating shape and motion with hierarchical priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 878–892 (2008)

    Article  Google Scholar 

  7. Akhter, I., Sheikh, Y., Khan, S., et al.: Nonrigid structure from motion in trajectory space. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 41–48 (2009)

    Google Scholar 

  8. Akhter, I., Sheikh, Y., Khan, S., et al.: Trajectory space: a dual representation for nonrigid structure from motion. IEEE Trans. Pattern Anal. Mach. Intell. 33(7), 1442–1456 (2011)

    Article  Google Scholar 

  9. Gotardo, P.F.U., Martinez, A.M.: Non-rigid structure from motion with complementary rank-3 spaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3065–3072 (2011)

    Google Scholar 

  10. Dai, Y., Li, H., He, M.: A simple prior-free method for non-rigid structure-from-motion factorization. Int. J. Comput. Vis. 107(2), 101–122 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Lee, M., Cho, J., Choi, C.H., et al.: Procrustean normal distribution for non-rigid structure from motion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1280–1287 (2013)

    Google Scholar 

  12. Gower, J.C.: Generalized procrustes analysis. Psychometrika 40(1), 33–51 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  13. Zelditch, M.L., Swiderski, D.L., Sheets, H.D.: Geometric Morphometrics for Biologists: A Primer. Academic Press, Cambridge (2012)

    MATH  Google Scholar 

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Acknowledgement

The work was supported by a grant from National Natural Science Foundation of China (No. 61370109), a key project of support program for outstanding young talents of Anhui province university (No. gxyqZD2016013), a grant of science and technology program to strengthen police force (No. 1604d0802019), and a grant for academic and technical leaders and candidates of Anhui province (No. 2016H090).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Anhui University, Hefei, China

    Xia Chen, Zhan-Li Sun, Tao Shen & Chao Zheng

  2. Department of Communication Technology, Electronic Information Engineering College, Suzhou Vocational University, Suzhou, China

    Shang Li

Authors
  1. Xia Chen
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  2. Zhan-Li Sun
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  3. Shang Li
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  4. Tao Shen
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  5. Chao Zheng
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Corresponding author

Correspondence to Zhan-Li Sun .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

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Chen, X., Sun, ZL., Li, S., Shen, T., Zheng, C. (2017). An Effective EM-PND Based Integrated Approach for NRSFM with Small Size Sequences. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-63315-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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